{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'click', 'hour', 'C1', 'banner_pos', 'site_id', 'site_domain',\n",
      "       'site_category', 'app_id', 'app_domain', 'app_category', 'device_id',\n",
      "       'device_ip', 'device_model', 'device_type', 'device_conn_type', 'C14',\n",
      "       'C15', 'C16', 'C17', 'C18', 'C19', 'C20', 'C21'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "dpath = \"./data/\"\n",
    "train_file = dpath + \"train.csv\"\n",
    "test_file = dpath + \"test\"\n",
    "sample_file = dpath + \"sampleSubmission\"\n",
    "data  = pd.read_csv(train_file,  nrows = 10)\n",
    "\n",
    "# Get the column names  得到列名，便于以后操作特征\n",
    "columns = data.columns\n",
    "print(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0    0    1    2         3         4         5         6  \\\n",
      "0           0  0.0  4.0  0.0  0.169331  0.164272  0.126192  0.124851   \n",
      "1           1  0.0  4.0  0.0  0.169331  0.164272  0.205633  0.205633   \n",
      "2           2  0.0  4.0  0.0  0.169331  0.164272  0.205633  0.205633   \n",
      "3           3  0.0  4.0  0.0  0.169331  0.164272  0.118826  0.122750   \n",
      "4           4  0.0  4.0  0.0  0.169331  0.164272  0.118826  0.122750   \n",
      "5           5  0.0  4.0  0.0  0.169331  0.183614  0.246505  0.245778   \n",
      "6           6  0.0  4.0  0.0  0.169331  0.164272  0.205633  0.205633   \n",
      "7           7  0.0  4.0  0.0  0.169331  0.164272  0.118826  0.122750   \n",
      "8           8  0.0  4.0  0.0  0.169331  0.164272  0.067349  0.067349   \n",
      "9           9  0.0  4.0  0.0  0.169331  0.164272  0.205633  0.205633   \n",
      "\n",
      "          7         8    ...           14        15        16        17  \\\n",
      "0  0.179579  0.198610    ...     0.169176  0.181125  0.147903  0.158608   \n",
      "1  0.208019  0.198610    ...     0.169176  0.181125  0.204211  0.158608   \n",
      "2  0.208019  0.198610    ...     0.169176  0.181125  0.204211  0.158608   \n",
      "3  0.128580  0.052148    ...     0.169176  0.181125  0.106150  0.158608   \n",
      "4  0.128580  0.305336    ...     0.169176  0.181125  0.223866  0.158608   \n",
      "5  0.179579  0.198610    ...     0.169176  0.181125  0.240306  0.158608   \n",
      "6  0.208019  0.198610    ...     0.169176  0.181125  0.264720  0.158608   \n",
      "7  0.128580  0.069516    ...     0.169176  0.044043  0.041359  0.359358   \n",
      "8  0.283003  0.198610    ...     0.169176  0.181125  0.067362  0.158608   \n",
      "9  0.208019  0.198610    ...     0.169176  0.181125  0.200999  0.158608   \n",
      "\n",
      "         18        19        20        21        22        23  \n",
      "0  0.158315  0.147559  0.145953  0.122205  0.164095  0.212944  \n",
      "1  0.158315  0.204211  0.158225  0.166803  0.193156  0.185462  \n",
      "2  0.158315  0.204211  0.158225  0.166803  0.193156  0.185462  \n",
      "3  0.158315  0.105414  0.145953  0.062193  0.095621  0.065921  \n",
      "4  0.158315  0.231712  0.158225  0.177263  0.192793  0.167681  \n",
      "5  0.158315  0.240306  0.294976  0.244163  0.192793  0.230343  \n",
      "6  0.158315  0.284650  0.294976  0.244163  0.233039  0.212944  \n",
      "7  0.421347  0.292750  0.145953  0.292750  0.185474  0.115511  \n",
      "8  0.158315  0.074190  0.145953  0.244163  0.192793  0.212944  \n",
      "9  0.158315  0.189605  0.158225  0.166803  0.192793  0.187744  \n",
      "\n",
      "[10 rows x 25 columns]\n"
     ]
    }
   ],
   "source": [
    "test = pd.read_csv('data/test_rate_no_na.csv',nrows=10)\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique values of column id is [5.21159374e+11 1.29622861e+12 1.39895883e+12 ... 1.84467432e+19\n",
      " 1.84467439e+19 1.84467440e+19]; total count is:(40428967,)\n",
      "unique values of column click is [0 1]; total count is:(2,)\n",
      "unique values of column hour is [14102100 14102101 14102102 14102103 14102104 14102105 14102106 14102107\n",
      " 14102108 14102109 14102110 14102111 14102112 14102113 14102114 14102115\n",
      " 14102116 14102117 14102118 14102119 14102120 14102121 14102122 14102123\n",
      " 14102200 14102201 14102202 14102203 14102204 14102205 14102206 14102207\n",
      " 14102208 14102209 14102210 14102211 14102212 14102213 14102214 14102215\n",
      " 14102216 14102217 14102218 14102219 14102220 14102221 14102222 14102223\n",
      " 14102300 14102301 14102302 14102303 14102304 14102305 14102306 14102307\n",
      " 14102308 14102309 14102310 14102311 14102312 14102313 14102314 14102315\n",
      " 14102316 14102317 14102318 14102319 14102320 14102321 14102322 14102323\n",
      " 14102400 14102401 14102402 14102403 14102404 14102405 14102406 14102407\n",
      " 14102408 14102409 14102410 14102411 14102412 14102413 14102414 14102415\n",
      " 14102416 14102417 14102418 14102419 14102420 14102421 14102422 14102423\n",
      " 14102500 14102501 14102502 14102503 14102504 14102505 14102506 14102507\n",
      " 14102508 14102509 14102510 14102511 14102512 14102513 14102514 14102515\n",
      " 14102516 14102517 14102518 14102519 14102520 14102521 14102522 14102523\n",
      " 14102600 14102601 14102602 14102603 14102604 14102605 14102606 14102607\n",
      " 14102608 14102609 14102610 14102611 14102612 14102613 14102614 14102615\n",
      " 14102616 14102617 14102618 14102619 14102620 14102621 14102622 14102623\n",
      " 14102700 14102701 14102702 14102703 14102704 14102705 14102706 14102707\n",
      " 14102708 14102709 14102710 14102711 14102712 14102713 14102714 14102715\n",
      " 14102716 14102717 14102718 14102719 14102720 14102721 14102722 14102723\n",
      " 14102800 14102801 14102802 14102803 14102804 14102805 14102806 14102807\n",
      " 14102808 14102809 14102810 14102811 14102812 14102813 14102814 14102815\n",
      " 14102816 14102817 14102818 14102819 14102820 14102821 14102822 14102823\n",
      " 14102900 14102901 14102902 14102903 14102904 14102905 14102906 14102907\n",
      " 14102908 14102909 14102910 14102911 14102912 14102913 14102914 14102915\n",
      " 14102916 14102917 14102918 14102919 14102920 14102921 14102922 14102923\n",
      " 14103000 14103001 14103002 14103003 14103004 14103005 14103006 14103007\n",
      " 14103008 14103009 14103010 14103011 14103012 14103013 14103014 14103015\n",
      " 14103016 14103017 14103018 14103019 14103020 14103021 14103022 14103023]; total count is:(240,)\n",
      "unique values of column C1 is [1001 1002 1005 1007 1008 1010 1012]; total count is:(7,)\n",
      "unique values of column banner_pos is [0 1 2 3 4 5 7]; total count is:(7,)\n",
      "unique values of column site_id is ['000aa1a4' '00255fb4' '003cf93d' ... 'ffdcedfa' 'fffde64d' 'fffe8e1c']; total count is:(4737,)\n",
      "unique values of column site_domain is ['000129ff' '0035f25a' '004d30ed' ... 'ffdec903' 'fff32e94' 'fff602a2']; total count is:(7745,)\n",
      "unique values of column site_category is ['0569f928' '110ab22d' '28905ebd' '335d28a8' '3e814130' '42a36e14'\n",
      " '50e219e0' '5378d028' '6432c423' '70fb0e29' '72722551' '74073276'\n",
      " '75fa27f6' '76b2941d' '8fd0aea4' '9ccfa2ea' 'a72a0145' 'a818d37a'\n",
      " 'bcf865d9' 'c0dd3be3' 'c706e647' 'da34532e' 'dedf689d' 'e787de0e'\n",
      " 'f028772b' 'f66779e6']; total count is:(26,)\n",
      "unique values of column app_id is ['000d6291' '000f21f1' '00110ae2' ... 'ffef3b38' 'fff00b38' 'fff4213a']; total count is:(8552,)\n",
      "unique values of column app_domain is ['001b87ae' '002e4064' '00314725' '030e4250' '03da86e1' '046a728e'\n",
      " '05975007' '063914ab' '0654b444' '06a7ad59' '084f6382' '090fcb2e'\n",
      " '09abdb18' '09ae65d1' '0b793b58' '0b7d3d7d' '0c3e87f7' '0cfa8592'\n",
      " '0d79ee56' '0e8616ad' '0eb1a401' '1020a5f7' '1102383a' '110d4421'\n",
      " '116fd53f' '117698cb' '11c6546c' '11f791ec' '12aaf71f' '12ea721c'\n",
      " '139260e0' '13b4d1e1' '13ed06ee' '1438d51f' '15c23f8e' '15ec7f39'\n",
      " '15f7400f' '15fa2703' '1615cf32' '1638b0a9' '17578ec3' '17810bfa'\n",
      " '179fcd69' '1833416a' '185fc975' '18e5bfbf' '18eb4e75' '19b11785'\n",
      " '1ac0a4ae' '1acab003' '1afd1880' '1b74784d' '1c37dcad' '1c57718f'\n",
      " '1c666936' '1c73b8ac' '1c895c8c' '1cb641ec' '1cbecd39' '1d1d4953'\n",
      " '1d5e09f4' '1dc4224a' '1dc9b529' '1ddc989f' '1e3b33ad' '1ea19ec4'\n",
      " '1eb35dd1' '1ed56ded' '1f606580' '201ad671' '2022d54e' '203a4d02'\n",
      " '20ab8b07' '214e1541' '224d4dec' '2347f47a' '23e2c80e' '24f896e0'\n",
      " '25628028' '256a5990' '2619a4f7' '26378630' '26894d3c' '27ee373d'\n",
      " '28269d80' '28a85b13' '298309ba' '29cd071c' '2a28c23d' '2b627705'\n",
      " '2bcc7b25' '2c11183d' '2c1c31c6' '2c94e6e5' '2d332391' '2d4a0207'\n",
      " '2e06c061' '2eb85c80' '2ee4f367' '2ef29720' '30e8a43f' '30f30cf0'\n",
      " '3114c619' '316edd1b' '3191f81f' '31d17fc8' '323165e8' '323f3fe5'\n",
      " '32f9558b' '337b74ad' '33da2e74' '3419dd80' '347bf088' '3630f389'\n",
      " '36358a3d' '366cee9d' '36fb59d6' '3741ede9' '38462a94' '38c660a5'\n",
      " '391ef1c3' '39e34dc5' '3a37fc9a' '3c08d416' '3c992420' '3ca588d4'\n",
      " '3cf50bc0' '3d459804' '3df323d8' '3e5459f6' '3e9b6bc2' '3f64fb96'\n",
      " '3fa331b0' '3feeed1e' '41c9b931' '424d97b0' '42ed489e' '435769ed'\n",
      " '438468ad' '43cf4f06' '44324ff4' '44367c4a' '448ca2e3' '449e219f'\n",
      " '4516163d' '45519326' '455b7630' '45a51db4' '47464e95' '47db8711'\n",
      " '4890a39e' '48aec236' '48e6f39d' '48f95f3e' '4966e53f' '4a4f8143'\n",
      " '4d86b5ce' '4e007635' '4e0bb613' '4e33d472' '4ed652f4' '4f983e50'\n",
      " '4fafd4c4' '5048f612' '505949a0' '51174fb1' '5151808a' '519a450d'\n",
      " '51d3e97f' '5211766b' '52a052f3' '52c29fe1' '52d64e90' '531c5e42'\n",
      " '535a6777' '53747a78' '5397e464' '5406e4db' '547624e0' '55240cf0'\n",
      " '5576cc84' '55888256' '55cb48a1' '56eabb45' '57cf0548' '57e92fff'\n",
      " '58214ae1' '5828b59d' '589f98f6' '59e465f4' '5aa9fd44' '5ab4623d'\n",
      " '5ac0b939' '5ad474ea' '5af432e2' '5b3f66ff' '5b46af43' '5b9c592b'\n",
      " '5ba508e3' '5bcedd7d' '5c5a694b' '5c620f04' '5c8af8e9' '5ce5b882'\n",
      " '5ced45bd' '5d1e6171' '5d4da5b0' '5da9e6db' '5daf29b2' '5ddae6d6'\n",
      " '5e6486c6' '5ef0e2e9' '605eb685' '61b00f7b' '621d726b' '63f57be0'\n",
      " '6437e20c' '64845f22' '64ae80a5' '655e300a' '659251d7' '6598724a'\n",
      " '65a98a05' '65b2af4c' '672111e6' '6793d46b' '67c844d8' '6879d53f'\n",
      " '69528475' '6a0a3a9d' '6a90b0cb' '6aafed40' '6b062297' '6bfb9168'\n",
      " '6c2d0c26' '6cf43c3b' '6dc31ecb' '6de3d639' '6dea6e92' '6ec102c5'\n",
      " '6f406d5b' '6f7ca2ba' '700adbf0' '70146488' '713c0c91' '721ddffd'\n",
      " '734d52b9' '7366e108' '73fc6786' '741bb270' '756ffb85' '7661e9ea'\n",
      " '76a8fa7c' '772df91c' '7801e8d9' '78051bc0' '788e5855' '790dd3cc'\n",
      " '7a0640b2' '7a492380' '7a9371fa' '7a94032f' '7a94af76' '7b3b7dad'\n",
      " '7b833eb9' '7bb90734' '7bbb38df' '7c17d732' '7c4d2cca' '7c88bca5'\n",
      " '7cf770da' '7d58e760' '7dab23c8' '7ecafa37' '7eec2ab0' '7f125d40'\n",
      " '800100e0' '8068d98f' '813f3323' '813fb413' '81b19a31' '828da833'\n",
      " '82e27996' '83ccf7b5' '8441d3ab' '863c4528' '863f4950' '86aa8fec'\n",
      " '86adec6c' '88293ffa' '885c7f3f' '88f50364' '89896125' '8a1d880a'\n",
      " '8ab0d4f4' '8af3e517' '8bb7dba9' '8bba499a' '8d87821d' '8e1c9078'\n",
      " '8e1f4a6e' '8e8ca1d0' '8e961dc9' '8f97c141' '8fbe42cb' '90322ef6'\n",
      " '90706f5d' '90d75517' '911c0d17' '916026d9' '92036a11' '927b53cd'\n",
      " '92895b94' '9299777a' '931b9f64' '938e40d3' '93f9275a' '94007c57'\n",
      " '9404f9fe' '946b7dcf' '9492ae41' '949683f7' '94f986fe' '95f77e10'\n",
      " '96c7bad2' '971c3ba7' '97d745c9' '97db6a0b' '97efe5c6' '9830a8fb'\n",
      " '999f1fac' '99b4c806' '99cce2d5' '9a9085ad' '9acfe436' '9ba5fbe6'\n",
      " '9c06a810' '9c566260' '9c875c2e' '9cc1123d' '9d65dcb9' '9d77cdb6'\n",
      " '9e4f24c2' '9e96e8cf' '9eaabdd7' '9ec164d3' '9ec8f0d8' '9ecca2dd'\n",
      " '9fc46d8f' '9fd3bf89' 'a09b1cc0' 'a21d5b1a' 'a21f883a' 'a271c340'\n",
      " 'a5204413' 'a5ab1a9f' 'a5f9dc5b' 'a67cb0c5' 'a696f7de' 'a6ec4ed6'\n",
      " 'a78efb9c' 'a7accd5b' 'a7c33f94' 'a841febe' 'a8985b53' 'a8b0bf20'\n",
      " 'a96b2ee7' 'a981375c' 'a9aab246' 'a9caeae7' 'aa396318' 'ab648479'\n",
      " 'ab6b3530' 'ad63ec9b' 'ad65a8a2' 'adacd5cc' 'adf95bcd' 'ae36b8ab'\n",
      " 'ae637522' 'aee494fb' 'aefc06bd' 'af201489' 'af237497' 'af524670'\n",
      " 'afdf1f54' 'b0920d40' 'b12ff13e' 'b1ab9955' 'b2816726' 'b28f54ad'\n",
      " 'b299335a' 'b2e11faa' 'b398ab59' 'b3c288b7' 'b408d42a' 'b43ef300'\n",
      " 'b4da5985' 'b4df0cc5' 'b50610ee' 'b51aefa8' 'b5f3b24a' 'b6c7e8e1'\n",
      " 'b6db2208' 'b72bf947' 'b7af3e0a' 'b7bbc1c1' 'b88e8096' 'b8d325c3'\n",
      " 'b8f3e522' 'b9215c20' 'b9528b13' 'b97def0d' 'b9f04b56' 'ba275770'\n",
      " 'bae91dfe' 'bb27eb10' 'bb2a164d' 'bb6bcbae' 'bb85b34d' 'bb8f7b3f'\n",
      " 'bcef5708' 'bd6843b4' 'bd8c1fdc' 'bdde4c9b' 'bdfa93d5' 'be452468'\n",
      " 'bf2623ab' 'c0940197' 'c0e0e9ca' 'c15f229b' 'c343527e' 'c36365e3'\n",
      " 'c41aa5ee' 'c5c485e8' 'c5c60bfd' 'c658acba' 'c6824def' 'c72257c6'\n",
      " 'c87b28e6' 'c91a8f07' 'c91cbbb4' 'c9373861' 'c9f545d5' 'ca7441a4'\n",
      " 'cab5b73d' 'cb36afb8' 'cb96370c' 'cd732fd5' 'cda96d46' 'ce031f02'\n",
      " 'ce2aa683' 'ce2ca36f' 'cea55998' 'ced163d2' 'cf26d810' 'cf49a27a'\n",
      " 'cf9adca5' 'd06cce07' 'd083df39' 'd15c637f' 'd1600859' 'd18c63a1'\n",
      " 'd22b6d64' 'd25f310a' 'd3934b52' 'd3c75c8f' 'd3e7c965' 'd4c7399f'\n",
      " 'd53e0703' 'd55bd9a0' 'd6feb1a4' 'd767dc6b' 'd8073046' 'd80e8488'\n",
      " 'd89240fd' 'd902c9ef' 'd95432fe' 'd98095bb' 'd9b5648e' 'd9e67b62'\n",
      " 'd9f4700d' 'da1be86e' 'da45f10e' 'db829551' 'dbf5ab77' 'dcb74110'\n",
      " 'dcdba109' 'dd9d354c' 'dec87c2e' 'dee05fca' 'df32afa9' 'df50ae88'\n",
      " 'df741103' 'e0881fef' 'e0be164b' 'e193e1f5' 'e1f23257' 'e24414c4'\n",
      " 'e25dadf0' 'e25ea824' 'e25eea83' 'e29fc2b7' 'e2fa941d' 'e4a3128e'\n",
      " 'e4ab3355' 'e51135b7' 'e559a22e' 'e5d008b4' 'e5d5313f' 'e6cf1c39'\n",
      " 'e7803a28' 'e787a6bc' 'e7928b55' 'e81561d1' 'e92089e6' 'e9616877'\n",
      " 'e9d5949e' 'ea4fcf79' 'eaaf6d12' 'ead20d3d' 'ebc4233e' 'ed0f64d8'\n",
      " 'ed7f6170' 'edc7d7a7' 'edd13025' 'ee50c840' 'ee7d2e2b' 'ef1fc174'\n",
      " 'f09de4dc' 'f1083d2a' 'f2140094' 'f2c88c63' 'f2f777fb' 'f34077eb'\n",
      " 'f3ad7798' 'f435cae0' 'f48b6635' 'f5a7c834' 'f75ccedd' 'f7eba725'\n",
      " 'f7f22564' 'f82a3e61' 'f84a2400' 'f91396ac' 'fb4fee83' 'fc41b20c'\n",
      " 'fc857957' 'fce7d5d4' 'fceed35e' 'fd0f197b' 'fd53c2dc' 'fd5f0ee2'\n",
      " 'fd68cbd8' 'fdbdd2a9' 'fe369646' 'fe5e664e' 'fea0d84a' 'ff191ca9'\n",
      " 'ff6630e0']; total count is:(559,)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique values of column app_category is ['07d7df22' '09481d60' '0bfbc358' '0d82db25' '0f2161f8' '0f9a328c'\n",
      " '18b1e0be' '2281a340' '2fc4f2aa' '4681bb9d' '4b7ade46' '4ce2e9fc'\n",
      " '52de74cf' '5326cf99' '6fea3693' '7113d72a' '71af18ce' '75d80bbe'\n",
      " '79f0b860' '86c1a5a3' '879c24eb' '8ded1f7a' '8df2e842' 'a3c42688'\n",
      " 'a7fd01ec' 'a86a3e89' 'bd41f328' 'bf8ac856' 'cba0e20d' 'cef3e649'\n",
      " 'd1327cf5' 'dc97ec06' 'ef03ae90' 'f395a87f' 'f95efa07' 'fc6fa53d']; total count is:(36,)\n",
      "unique values of column device_id is ['00000414' '00000715' '00000919' ... 'ffffde2c' 'ffffe321' 'ffffe5da']; total count is:(2686408,)\n",
      "unique values of column device_ip is ['0000016d' '00000262' '00000911' ... 'fffff971' 'fffff9d2' 'fffffaa3']; total count is:(6729486,)\n",
      "unique values of column device_model is ['00097428' '0009f4d7' '000ab70c' ... 'ffe72be2' 'ffeafe15' 'fffc15b0']; total count is:(8251,)\n",
      "unique values of column device_type is [0 1 2 4 5]; total count is:(5,)\n",
      "unique values of column device_conn_type is [0 2 3 5]; total count is:(4,)\n",
      "unique values of column C14 is [  375   376   377 ... 24049 24050 24052]; total count is:(2626,)\n",
      "unique values of column C15 is [ 120  216  300  320  480  728  768 1024]; total count is:(8,)\n",
      "unique values of column C16 is [  20   36   50   90  250  320  480  768 1024]; total count is:(9,)\n",
      "unique values of column C17 is [ 112  122  153  178  196  394  423  479  544  547  549  550  571  572\n",
      "  576  613  644  686  761  768  827  832  863  872  873  898  901  906\n",
      "  937 1008 1042 1076 1092 1107 1141 1149 1160 1161 1174 1248 1253 1255\n",
      " 1272 1401 1426 1447 1507 1515 1516 1526 1528 1637 1685 1694 1698 1702\n",
      " 1722 1740 1741 1752 1769 1780 1784 1800 1809 1821 1823 1835 1863 1872\n",
      " 1873 1882 1884 1887 1895 1899 1903 1921 1926 1932 1934 1939 1946 1955\n",
      " 1960 1965 1972 1973 1974 1991 1993 1994 1996 2009 2016 2036 2039 2043\n",
      " 2060 2083 2084 2101 2104 2150 2153 2154 2158 2161 2162 2181 2187 2199\n",
      " 2201 2206 2218 2225 2226 2227 2229 2242 2250 2253 2260 2263 2264 2270\n",
      " 2271 2278 2279 2281 2282 2283 2284 2285 2286 2289 2291 2292 2295 2299\n",
      " 2303 2304 2306 2307 2312 2316 2323 2325 2331 2333 2338 2339 2340 2346\n",
      " 2348 2351 2369 2371 2372 2374 2375 2390 2394 2397 2412 2418 2420 2421\n",
      " 2424 2425 2427 2428 2429 2434 2435 2436 2438 2439 2440 2441 2443 2446\n",
      " 2447 2448 2449 2450 2451 2453 2454 2455 2459 2462 2465 2467 2471 2476\n",
      " 2478 2479 2480 2481 2482 2483 2485 2487 2489 2491 2492 2493 2494 2495\n",
      " 2496 2497 2498 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510\n",
      " 2511 2512 2513 2515 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527\n",
      " 2528 2530 2531 2532 2533 2534 2535 2536 2537 2539 2541 2542 2543 2544\n",
      " 2545 2546 2547 2548 2550 2551 2552 2553 2554 2555 2556 2557 2558 2560\n",
      " 2561 2563 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576\n",
      " 2577 2578 2579 2580 2581 2582 2583 2585 2587 2588 2589 2590 2591 2592\n",
      " 2593 2594 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608\n",
      " 2609 2610 2611 2612 2613 2614 2615 2616 2617 2619 2620 2624 2625 2627\n",
      " 2630 2631 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644\n",
      " 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658\n",
      " 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672\n",
      " 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686\n",
      " 2687 2688 2689 2691 2695 2698 2699 2700 2702 2703 2704 2705 2706 2707\n",
      " 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721\n",
      " 2722 2724 2725 2726 2727 2728 2729 2730 2735 2736 2737 2738 2740 2741\n",
      " 2742 2743 2744 2745 2746 2747 2748 2749 2752 2753 2754 2755 2756 2757\n",
      " 2758]; total count is:(435,)\n",
      "unique values of column C18 is [0 1 2 3]; total count is:(4,)\n",
      "unique values of column C19 is [  33   34   35   38   39   41   43   45   47  161  163  167  169  171\n",
      "  175  289  290  291  295  297  299  303  417  419  423  425  427  431\n",
      "  545  547  551  553  555  559  673  675  677  679  681  683  687  801\n",
      "  803  809  811  813  815  935  937  939  943 1059 1063 1065 1071 1195\n",
      " 1315 1319 1327 1447 1451 1575 1583 1711 1831 1835 1839 1959]; total count is:(68,)\n",
      "unique values of column C20 is [    -1 100000 100001 100002 100003 100004 100005 100006 100008 100010\n",
      " 100012 100013 100016 100019 100020 100021 100022 100024 100025 100026\n",
      " 100027 100028 100029 100031 100032 100033 100034 100037 100039 100040\n",
      " 100041 100043 100046 100048 100049 100050 100051 100052 100053 100054\n",
      " 100055 100056 100057 100058 100059 100060 100061 100062 100063 100064\n",
      " 100065 100068 100070 100071 100072 100073 100074 100075 100076 100077\n",
      " 100078 100079 100081 100082 100083 100084 100086 100087 100088 100090\n",
      " 100091 100093 100094 100095 100096 100097 100098 100099 100100 100101\n",
      " 100103 100105 100106 100107 100108 100109 100111 100112 100113 100114\n",
      " 100117 100119 100121 100122 100123 100124 100126 100128 100130 100131\n",
      " 100132 100133 100134 100135 100137 100138 100139 100141 100143 100144\n",
      " 100148 100149 100150 100151 100152 100153 100155 100156 100157 100160\n",
      " 100161 100162 100163 100165 100166 100168 100169 100170 100171 100172\n",
      " 100173 100175 100176 100177 100178 100179 100181 100182 100183 100185\n",
      " 100186 100187 100188 100189 100190 100191 100192 100193 100194 100195\n",
      " 100198 100199 100200 100202 100205 100206 100209 100210 100212 100213\n",
      " 100215 100217 100221 100224 100225 100228 100229 100233 100241 100244\n",
      " 100246 100248]; total count is:(172,)\n",
      "unique values of column C21 is [  1  13  15  16  17  20  23  32  33  35  42  43  46  48  51  52  61  68\n",
      "  69  70  71  76  79  82  85  90  91  93  94  95 100 101 102 104 108 110\n",
      " 111 112 116 117 126 156 157 159 163 171 177 178 182 194 195 204 212 219\n",
      " 221 229 246 251 253 255]; total count is:(60,)\n"
     ]
    }
   ],
   "source": [
    "# Explore every column's data distribution 探索每一列特征的分布情况\n",
    "for column in columns:\n",
    "    data_single_col = pd.read_csv(train_file, usecols = [column])\n",
    "    unique_vals = np.unique(data_single_col)\n",
    "    \n",
    "    print(\"unique values of column {} is {}; total count is:{}\".format(column, unique_vals[0,1,-1], unique_vals.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取全部数据\n",
    "data = pd.read_csv(train_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 随机下采样 取0.05的train数据\n",
    "np.random.seed(999)\n",
    "r1 = np.random.uniform(0, 1, data.shape[0])  #产生0～40M的随机数\n",
    "data = data.iloc[r1 < 0.20, :]\n",
    "data.to_csv('train_0.20.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8085811 entries, 2 to 40428960\n",
      "Data columns (total 24 columns):\n",
      "id                  float64\n",
      "click               int64\n",
      "hour                int64\n",
      "C1                  int64\n",
      "banner_pos          int64\n",
      "site_id             object\n",
      "site_domain         object\n",
      "site_category       object\n",
      "app_id              object\n",
      "app_domain          object\n",
      "app_category        object\n",
      "device_id           object\n",
      "device_ip           object\n",
      "device_model        object\n",
      "device_type         int64\n",
      "device_conn_type    int64\n",
      "C14                 int64\n",
      "C15                 int64\n",
      "C16                 int64\n",
      "C17                 int64\n",
      "C18                 int64\n",
      "C19                 int64\n",
      "C20                 int64\n",
      "C21                 int64\n",
      "dtypes: float64(1), int64(14), object(9)\n",
      "memory usage: 1.5+ GB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>click</th>\n",
       "      <th>hour</th>\n",
       "      <th>C1</th>\n",
       "      <th>banner_pos</th>\n",
       "      <th>device_type</th>\n",
       "      <th>device_conn_type</th>\n",
       "      <th>C14</th>\n",
       "      <th>C15</th>\n",
       "      <th>C16</th>\n",
       "      <th>C17</th>\n",
       "      <th>C18</th>\n",
       "      <th>C19</th>\n",
       "      <th>C20</th>\n",
       "      <th>C21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.009850e+05</td>\n",
       "      <td>100985.000000</td>\n",
       "      <td>1.009850e+05</td>\n",
       "      <td>100985.000000</td>\n",
       "      <td>100985.000000</td>\n",
       "      <td>100985.000000</td>\n",
       "      <td>100985.00000</td>\n",
       "      <td>100985.000000</td>\n",
       "      <td>100985.000000</td>\n",
       "      <td>100985.000000</td>\n",
       "      <td>100985.000000</td>\n",
       "      <td>100985.000000</td>\n",
       "      <td>100985.000000</td>\n",
       "      <td>100985.000000</td>\n",
       "      <td>100985.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.257598e+18</td>\n",
       "      <td>0.170035</td>\n",
       "      <td>1.410256e+07</td>\n",
       "      <td>1004.964490</td>\n",
       "      <td>0.289964</td>\n",
       "      <td>1.013824</td>\n",
       "      <td>0.33398</td>\n",
       "      <td>18852.297282</td>\n",
       "      <td>318.995138</td>\n",
       "      <td>60.028975</td>\n",
       "      <td>2113.333931</td>\n",
       "      <td>1.435094</td>\n",
       "      <td>226.978145</td>\n",
       "      <td>53233.589890</td>\n",
       "      <td>83.408387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.313642e+18</td>\n",
       "      <td>0.375666</td>\n",
       "      <td>2.964094e+02</td>\n",
       "      <td>1.076487</td>\n",
       "      <td>0.507307</td>\n",
       "      <td>0.519225</td>\n",
       "      <td>0.85860</td>\n",
       "      <td>4936.856485</td>\n",
       "      <td>22.246421</td>\n",
       "      <td>47.274026</td>\n",
       "      <td>607.128718</td>\n",
       "      <td>1.325145</td>\n",
       "      <td>351.274967</td>\n",
       "      <td>49956.040515</td>\n",
       "      <td>70.312629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.155413e+13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.410210e+07</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>375.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.667736e+18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.410230e+07</td>\n",
       "      <td>1005.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>16920.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1863.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.273293e+18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.410260e+07</td>\n",
       "      <td>1005.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>20346.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2323.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>100048.000000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.386164e+19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.410281e+07</td>\n",
       "      <td>1005.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>21894.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2526.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>100094.000000</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.844634e+19</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.410302e+07</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>24043.000000</td>\n",
       "      <td>1024.000000</td>\n",
       "      <td>1024.000000</td>\n",
       "      <td>2757.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1839.000000</td>\n",
       "      <td>100248.000000</td>\n",
       "      <td>255.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          click          hour             C1  \\\n",
       "count  1.009850e+05  100985.000000  1.009850e+05  100985.000000   \n",
       "mean   9.257598e+18       0.170035  1.410256e+07    1004.964490   \n",
       "std    5.313642e+18       0.375666  2.964094e+02       1.076487   \n",
       "min    3.155413e+13       0.000000  1.410210e+07    1001.000000   \n",
       "25%    4.667736e+18       0.000000  1.410230e+07    1005.000000   \n",
       "50%    9.273293e+18       0.000000  1.410260e+07    1005.000000   \n",
       "75%    1.386164e+19       0.000000  1.410281e+07    1005.000000   \n",
       "max    1.844634e+19       1.000000  1.410302e+07    1012.000000   \n",
       "\n",
       "          banner_pos    device_type  device_conn_type            C14  \\\n",
       "count  100985.000000  100985.000000      100985.00000  100985.000000   \n",
       "mean        0.289964       1.013824           0.33398   18852.297282   \n",
       "std         0.507307       0.519225           0.85860    4936.856485   \n",
       "min         0.000000       0.000000           0.00000     375.000000   \n",
       "25%         0.000000       1.000000           0.00000   16920.000000   \n",
       "50%         0.000000       1.000000           0.00000   20346.000000   \n",
       "75%         1.000000       1.000000           0.00000   21894.000000   \n",
       "max         7.000000       5.000000           5.00000   24043.000000   \n",
       "\n",
       "                 C15            C16            C17            C18  \\\n",
       "count  100985.000000  100985.000000  100985.000000  100985.000000   \n",
       "mean      318.995138      60.028975    2113.333931       1.435094   \n",
       "std        22.246421      47.274026     607.128718       1.325145   \n",
       "min       120.000000      20.000000     112.000000       0.000000   \n",
       "25%       320.000000      50.000000    1863.000000       0.000000   \n",
       "50%       320.000000      50.000000    2323.000000       2.000000   \n",
       "75%       320.000000      50.000000    2526.000000       3.000000   \n",
       "max      1024.000000    1024.000000    2757.000000       3.000000   \n",
       "\n",
       "                 C19            C20            C21  \n",
       "count  100985.000000  100985.000000  100985.000000  \n",
       "mean      226.978145   53233.589890      83.408387  \n",
       "std       351.274967   49956.040515      70.312629  \n",
       "min        33.000000      -1.000000       1.000000  \n",
       "25%        35.000000      -1.000000      23.000000  \n",
       "50%        39.000000  100048.000000      61.000000  \n",
       "75%       171.000000  100094.000000     101.000000  \n",
       "max      1839.000000  100248.000000     255.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0acbeaa385f751fd' '51cedd4e85f751fd' 'ecad23861fbe01fe' ...\n",
      " '7e7baafa85f751fd' '9c13b41985f751fd' '54c5d54585f751fd']\n"
     ]
    }
   ],
   "source": [
    "print(np.add(data.app_id.values, data.site_id.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对字符型类别先进行Label encoder，用来分析分布情况\n",
    "columns_str = ['site_id','site_domain','site_category','app_id','app_domain','app_category','device_id','device_ip','device_model']\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-59-3fe55f321b2c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcolumns_small\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     g = sns.factorplot(\"click\", col=column, col_wrap=6,\n\u001b[0;32m----> 9\u001b[0;31m                     \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_file\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'click'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m                     kind=\"count\", size=2.5, aspect=.8)\n\u001b[1;32m     11\u001b[0m     \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticklabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    707\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 709\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    711\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 455\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    456\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    457\u001b[0m         \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1067\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'skipfooter not supported for iteration'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1069\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1070\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1071\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'as_recarray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1837\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1838\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1839\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1840\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1841\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_first_chunk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.read\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_low_memory\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_column_data\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_tokens\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_with_dtype\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/dtypes/common.py\u001b[0m in \u001b[0;36mis_integer_dtype\u001b[0;34m(arr_or_dtype)\u001b[0m\n\u001b[1;32m    775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 777\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mis_integer_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr_or_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    778\u001b[0m     \"\"\"\n\u001b[1;32m    779\u001b[0m     \u001b[0mCheck\u001b[0m \u001b[0mwhether\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mprovided\u001b[0m \u001b[0marray\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mof\u001b[0m \u001b[0man\u001b[0m \u001b[0minteger\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a8982f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a88cae550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a881640f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对字符型特征，探索其与点击率的分布情况\n",
    "#选择取值数量比较少的几个特征\n",
    "columns_small = [\n",
    "    'C1','banner_pos','site_category','app_category',\n",
    "                 'device_type','device_conn_type',\n",
    "                'C15','C16','C18']\n",
    "for column in columns_small:\n",
    "    g = sns.factorplot(\"click\", col=column, col_wrap=6,\n",
    "#                     data=pd.read_csv(train_file,usecols=['click',column]),\n",
    "                       data = data,\n",
    "                    kind=\"count\", size=2.5, aspect=.8)\n",
    "    g.set_xticklabels(step=2)\n",
    "    g.savefig(\"./EDA/Cross_click_\"+column+\".png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b93dfc4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a55c30da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b7348f5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b734af828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c05046278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RrewNSwTDathWDOx6AGZNr6OrZ4BtewcGSyN3P7M14j2ZuJ29GfaEJZ0NO/axozfDu06Zx1FtjTz3ejcwVBJJJgwD+jL5wdee3rx7Stot8abAloPW1TPAshe2D/79109vobs/R77g/KTjVZ7YFITRGQvaOGNB2+B6dSP2sOsHHzt7Bnj2tT0YcN5x7fx2zdbBE5bXP7CWa+59odK7NiH92TyXXv8wl//7CvIF57ENwTeIsxa1sXhhGx6eWyzOIWJmpJLGc1u6GcgVmNNSzwtb9zIQDv/bsqePZ17bMyX7IvGiwJYxuTsbunrxMIXyBeejt63mwzc/xv1rtgFw+8qNvKF9Gm994yx+uOpVVm/cRUM6wQlzmzl9Qevge41cEgkCu316Pf3ZAqs37mLR7Gl86K0L6e7PsXx9F09s2sW1973EDcvWs2L9UJmkc+9AJEMB3Z1cSc35+49uZPOuPl7a1sMdj29m9YZdzGhM88b26Zy5aObgesU5RCDY9529GZrqklzy5nnkCs6aLd30Z/PcsmIDP1y1abDWX9xm8ZjL4S+qcxJjBraZNZjZr83sKTO7zYpXQEhV2D8U+jJ5vvPwK6zbvnfw9S/e9RznX/sg1973Iu7OLSs2sHrjLtqa0nzhzmdY9cpOHt+0myvPXsAHzlnIa7uDk2qnzm8lnUxw+tFDPex0aoTAbq4LHqcHj3v6spw8r4Vzj51Nc32KXz+1hX/45XPMaann6JmNfPGXz5HNF1ixrou3ff0B3vetR+juzw6+7/bu/rJO29qfzbP0ttWc+7UHeH5LN939WW5Yto4lx87mzUe38s3fvsQjL+9g8cI2EgnjrEXB/ppBfWp4YAOcMn8Gi2ZNY0Zjmqc37+Ge57aytz9Hc32Kz/38afqzebbs6eOi6x7iA99eOWzfXt/dp8vbD0Pbuvv5sxtX8P1HN1Z8W+Nd6fhBYLO7X2xmvwbeAdxXqcZk8wW6egaoTyVpbUqTMAuHeAU3cC39vHB3ovz8KAZb6TbzBccYutQagtCrSyUGA6lQcPb0ZWlpTA8u68vk2dOXpb25nmQi2MftewcYyBaY19pAKhncUmp9Zw/1qQSLZk0jlUywvbuf57Z0M6e5gePmTAfg2de7eX5LNyfObeHkeS3s7c9x//Pb2LCjl7f9QTtnLZrJ05t388NVm+gdyPNnZx7F2cfM5LZHN3Ljg+uZOa2OT7/zOBbNmsb/+vGTrNvew9dTCf7+4pNYv72HWx/ZyAlHNnPDsvXs3pfljsc3c8EJR/A3Fx7HZf+2nA9/bxV1qQTvPeMoptWnmD29jq6eDKcvDHrWM5rSvLF9Gus7ew8Yh23AzGnFwA562ga8ad4M6lNJLjxpDnc8vhmA6644jaa6FH95awef/dnT/ObZLRzR3MBzr+/hz7+zii+/+2T+5YG1/HbNNt40r4UvXvImTj1qBh0bdrF2+15OX9DGKfNnDP4bQPD7tqMnQ9u0NPWpJBu6evnOw6/w2IadXHzqXN592nz+90+fYtWGnbQ11fH+bz3Ckj+Yza59WT530Ql092e58j9WAnDF2UcDcNwRzTQ3pMjmC8N+V4qjYk49qpWEGaceNYPl67ooOJz7xlkcN6eZ763YwNW/eJYV63ewe1+G9Z09XPGtR7nm8lO56fcv88snX2d+ayNXX3wi5xwzizse38zvXurk3GNnc/mZR1GfTrJ8XRcbd/RyzjGzOGX+DDL5wmBP/uT5M2hpSJPLF9i8q4+6VIIjWxpIJIxCwdndl2VafXLwCs3iMarlu+S4OwUf6mDk8gU27dyHmbFgZhPPb+nmI7d00N2f5Yjm+oq3Z7zAvgC4I3z+APBHVCCwl724nW/c9yI7ezMU+0Z1yQTT6pP0DOTI5p2kGdMbUiQMvnL38/RmcrQ0pGlrSpPNB6GYyRdoaUjT0pCiL5unOzwp1NKYpjGdZO9Ajr39WdLJBC0NadJJY09flt6B4A7XMxrTOM7u3iz7snlaGlK0NtXRn82zszdDNl+gramOlsY03X1Zdu7LkDCjramO6fVJunoy9AzkSCaM2dPrSCUSbN/bH7Q/YcxprieTH5qhLpUw5rQ0sHtfht5M0HNKJ40jmhvYWtJTLI6iKM67AcEIhFTCBk/oQTBEri+bDz5IDG5Ytp7GdJK+bJ6muiSN6ST/+cwWUgkjV3Deflw72/b08/HbnwDgiOZ6/vW/n85POl7l6l88C8BHlhzDF/7biXzhzmf4wcpNNNen+Mp7TuHIGQ187Lw3cMOy9Vx22jzawuC9fPHR3Pjg+mG16zMWtAWBnRg+rK9tWnCMAFqb6kiacdTMRloa0wBcdPKR3PnEa5xzzEwuffM8AM4/vp07n3iN4+c084O/PIcnNu3mr3+wmkuuf5imuiRXnXsM9zy7hfd96xHqUolhJZPWpjTt0+vJu9M7kGP73oHBevMRzUENPZ1IcOLcZq697yWuve8lkgnj/73/NM5c2MYHv72Su5/ZysWnzuXk+TOAoNb+u5c6WbwwKIUkEsaZC9t48tXhJxWD37kUC2c1AUFwP7S2i7amNO846UjqUgn+9Iz5/HT1ZmZNq+PHH/1DunoG+KvvP867/uVh6lIJ/ueSY1i+rouPff9xkgkjX3AWzGziobUv8I37XgSG34l+RmOa3oEcuZJvHPNmNNDZMzC4XmM6yazpdWwvKS+1N9fTkE7QtTcz+Lsza3od7sE3oIFcgZaGFC0NaTL5Ar0DOQoe/P41pBNk8gX6swUSFv6eJhNk8wWyuQJO8KFsZpgF30QSZsOXES4zCF6JnuN09+XY0Rscq9amNC0Nabbu6R+8F2ldKgEefDv82cfeyknzWireLhurTmZm9wLXuPv9ZvYR4Cx3/+h+6ywFloZ/PR548RDbMhuIxziu8tE+1wbtc22YzD4vdPf28VYar4fdBcwIn88YqTHufhNw00E3bz9m1uHuiyf7PnGifa4N2ufaEMU+j1ec+i/gneHzC4BllWyMiIiMbrzA/gEw38yeBnYSBLiIiEyBMUsi7j4AXBxRWyZdVokh7XNt0D7Xhorv85gnHUVE5PBRuwMsRURiZsoDu5avpjSztJn9aqrbERUzu8XMHjWzu8ys6m9PZ2YpM/upmS03s+9OdXuiZGafMrP7p7odUTCzs8xss5k9HP45vlLbmvLAZuhqyjcDbQRXU1Y9M2sEVlM7+7sESLn7W4AWhkYfVbPLgKfc/VxgrpmdNtUNioKZLQQ+NNXtiFAbcKO7Lwn/HOq1KOM6HAL7AuC34fPi1ZRVz9373P1UYPNUtyUi24DrwueHw+9dFO4B/jn8NtEKdE9xe6JyHfD5qW5EhNqA95rZKjO7o5JVgsPhP84soDi3ZDcwc4x1Jabcfa27rzKz9wAFKjgnzeHC3XvcfR+wHNjm7i9PdZsqzcyuBJ4C1kx1WyK0Drja3c8G5gLnVWpDh0Ngj3s1pVQHM7sU+CRwibvnxls/7sxslpnVA28F2sysFr49Xgz8MfAj4Ewz+/gUtycKG4D7S54fUakNHQ6Braspa4CZHQl8BrjY3Wvl/lifBi539zywD2ic4vZUnLtf6e5LgCuA1e5+/VS3KQKfAq4wswRwMvBspTZ0OAS2rqasDR8i+Lp4b3gm/aqpblAEbgCuMrNHgB3AvVPcHqmM64EPAyuBO929YuUgXTgjIhITh0MPW0REJkCBLSISEwpsEZGYUGCLiMSEAltEJCYU2FXIzL5kZucf5M8caWZfqFCTKsLMzjezRRV8/wfNrCP8866S5RM6VmZ2mZm1TmC900uHOZrZgwfRxgO2Ecd/S5kYBbYA4O5b3f0rU92Og3Q+sKjC27gMeD/wfTObBQd1rC4jmENkTO7+hLsf6mx+B2wjpv+WMgEK7CphZq1m9lsz+x3w9nDZHDO7x8xWmtnnw2V3mdlR4fOfmdmC8PkiM7u55P3eaGbLwt7lV8Jlx5X0Ov9ijLYsNrMVZvaEmf11uOyMcNljZvaBcNmDZvaZcNKcu8JlN5vZ1Wb2SLh+wyjbuBW4CvhXM/tRuOwGM7swfH69mV0Yvt/Pw2Nw3WjHZSzuvh54DHjLKMfq+HAK1cfM7O/CZfcBFwE/NbNvhsvON7N/Do/7l0t+/nwz+1LJJhvDdZ40s/cVj1XJ+jeHbThgG6O075jw33KVmX3mYI6zHF4U2NVjKXC3u59HMLkSBDOm/cjdzwHeHfYQ7wD+xMzSQIu7bxrl/a4BvhDeBbrBzKYDXwe+RDA3xt+OMSvZDQSXJi8GTilZ9kFgCfDZkq/x/eGkOc1mNi9c1urufwi8CJwx0gbc/S+A7wKfcPcrwsW3Ah8wsyRBuD4QLv95eAyOM7MzRjku4+kimJVtJBeH2zgL2BS2753AbwguTf+bknXfD3ze3f9+jG3NAv4WeBvwT6Md5zG2sb9rgH8gOCYXmdmJ4fJxj7McXhTY1eMNBLOkQdAbBDge+KuwdzYdmAf8kmByniUMTWs7khNK3uezQG/4fv+HYKa9JKN/3W9z903hHBqfDJfNcveXw/uEPg8cEy7/Xvi4EagbY9m43H0lcBJBr/N+dy9+cBX348lwuyMdl/HMBHaN8tptwJvM7Nfh+43lh+6+dpx1Ot19fTjnShcHzmB5sHOSnAg8Eh6PVQT/tnCIx1mmjgK7emwimHgGhnpLLwKfc/fzgWuBXe6+GzDgEuBnY7zfC8DZ4fN7gGPD9/sf4fv9O5AZ5Wd3mdkCCybDedLMmoCu8Kt6HUFgvALBFKQj/PxIy0bSB0wDKOmF3kUwt8OtJeudEz6eAaxnhOMy1kbCE5tnAitGWeUC4KvApQTfPNL7t6/ERPat3cwWmtk0gpnfdgEZM2sPl72tZN2RtrG/NcBbwmN0FsEH5kTbIocRBXb1uAn4UzN7mKH/wF8FPmNmjwIXAlvD5fcBZ7v7K2O832eBr4Q/uzzsFX4O+I6ZdQBHu3vvKD/7CeDHwKPAdeGc0J8AbieYG/qa8INjsu4APm9mKwm+YRSXde03Ac+7wnXWuPuTjH5cRnIn8EPgCncfLdjXEfSyHwPucfdsuPxWguP1mAV3GJqoXQQfOiuAL4U941uBW4BvMHw2uIls47PAPxJMTnSPu79wEG2Rw4gmf5KqYWbnAv8G/KO7/yxcdjNB6G2YwqaJlIUCWw6ZmV0E/N1+i3/h7teWcRtHcmDpZqO7f6Bc2xCJCwW2iEhMqIYtIhITCmwRkZhQYIuIxIQCW0QkJhTYIiIx8f8BFoB69uznXnUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5a656710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bfd5cef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ba1137c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c6cf6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ab1ce79b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a174d1ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a174d15c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac3487470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bb9516e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示出每个特征在每个分类下的的分布情况\n",
    "columns_num = ['click', 'hour', 'C1', 'banner_pos', 'device_type', 'device_conn_type', 'C14', 'C15', 'C16', 'C17', 'C18', 'C19', 'C20','C21']\n",
    "\n",
    "for column in columns_num:\n",
    "    fig = plt.figure()\n",
    "    sns.distplot(data[column].values, bins=30)\n",
    "    plt.xlabel(\"{} Distribution\".format(column, fontsize=12))\n",
    "    plt.savefig('./EDA/'+column+' distribution.png')\n",
    "    plt.show()\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a889efef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    " \n",
    "# create data\n",
    "x = data.app_category.values\n",
    "y = data.C1.values\n",
    "z = data.click.values\n",
    " \n",
    "# Change color with c and alpha. I map the color to the X axis value.\n",
    "plt.scatter(x, y, s=z*2000, cmap=\"Blues\", alpha=0.4, edgecolors=\"grey\", linewidth=2)\n",
    " \n",
    "# Add titles (main and on axis)\n",
    "plt.xlabel(\"the X axis\")\n",
    "plt.ylabel(\"the Y axis\")\n",
    "plt.title(\"A colored bubble plot\")\n",
    " \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribution of Hour')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9323fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]), data.hour.values,color='purple')\n",
    "plt.title(\"Distribution of Hour\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19cb28d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.C1.values, bins=30, kde=False)\n",
    "plt.xlabel('crime rate', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 两两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the names of all the columns\n",
    "cols = data.columns\n",
    "\n",
    "# Calculates pearson co-efficient for all combinations\n",
    "data_corr = data.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.35875046e-04 4.31310808e-03 7.73826922e-03 2.22492469e-02\n",
      " 2.60641251e-02 3.26159625e-02 3.64783748e-02 3.76863189e-02\n",
      " 5.58973859e-02 5.77885159e-02 6.04294172e-02 7.06728966e-02\n",
      " 8.33885096e-02 1.30002277e-01 1.00000000e+00]\n"
     ]
    }
   ],
   "source": [
    "print(np.sort(data_corr.click.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a8beb9710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(data_corr, annot=True)\n",
    "\n",
    "sns.heatmap(data_corr, cbar=False)\n",
    "plt.savefig(dpath+'CTR_coor.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1,1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[0.319449057556]</td>\n",
       "      <td>RM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[0.229843609595]</td>\n",
       "      <td>RAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.11164947562]</td>\n",
       "      <td>ZN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[0.0512456938248]</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.0265359794548]</td>\n",
       "      <td>CHAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-0.0379094943747]</td>\n",
       "      <td>INDUS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[-0.0552504538354]</td>\n",
       "      <td>AGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[-0.0829425704712]</td>\n",
       "      <td>CRIM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-0.159177260109]</td>\n",
       "      <td>NOX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[-0.215616780333]</td>\n",
       "      <td>PTRATIO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-0.255123757229]</td>\n",
       "      <td>TAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[-0.313173772457]</td>\n",
       "      <td>DIS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[-0.372759366843]</td>\n",
       "      <td>LSTAT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  coef  columns\n",
       "5     [0.319449057556]       RM\n",
       "8     [0.229843609595]      RAD\n",
       "1      [0.11164947562]       ZN\n",
       "11   [0.0512456938248]        B\n",
       "3    [0.0265359794548]     CHAS\n",
       "2   [-0.0379094943747]    INDUS\n",
       "6   [-0.0552504538354]      AGE\n",
       "0   [-0.0829425704712]     CRIM\n",
       "4    [-0.159177260109]      NOX\n",
       "10   [-0.215616780333]  PTRATIO\n",
       "9    [-0.255123757229]      TAX\n",
       "7    [-0.313173772457]      DIS\n",
       "12   [-0.372759366843]    LSTAT"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.730555975545\n",
      "The r2 score of LinearRegression on train is 0.787378457363\n"
     ]
    }
   ],
   "source": [
    "print ('The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr))\n",
    "print ('The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1c54e358>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ufdIRMSIilgCPAXdn5v3VqMsjYmlEfDEiRnXz3gsiYlFELPI+qZIk1a+ukM7MTZnZAkwC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\nWpKkQv1/uWyOCNdq0vcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1baf5b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7,5))\n",
    "f.tight_layout()\n",
    "ax.hist(y_train - y_train_pred_lr, bins=40, label='Residuals Linear', color='b', alpha=.5)\n",
    "ax.set_title(\"Histgram of Residuals\")\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tU9XoHjij3ZJIpm087Nuyif2fv0fXky4kv/cA8ntHv/nP+F4vFqwpj5naH63yHLSvMgqt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c56b9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3))\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([-3,3], [-3,3], '--k')\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-0.08229312,  0.11129155, -0.03764302,  0.02636709, -0.15868757,\n",
       "        0.31873648, -0.05506665, -0.31363272,  0.23015066, -0.25481044,\n",
       "       -0.21506575,  0.05058413, -0.37238701])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDRegressor\n",
    "\n",
    "sgdr = SGDRegressor(max_iter=1000)\n",
    "\n",
    "sgdr.fit(X_train, y_train)\n",
    "\n",
    "sgdr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of default measurement of SGDRegressor on test is 0.729847721423\n",
      "The value of default measurement of SGDRegressor on train is 0.78736530141\n"
     ]
    }
   ],
   "source": [
    "print ('The value of default measurement of SGDRegressor on test is', sgdr.score(X_test, y_test))\n",
    "print ('The value of default measurement of SGDRegressor on train is', sgdr.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.728011409006\n",
      "The r2 score of RidgeCV on train is 0.786279498993\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "alphas = [1.0, 10.0]#[0.01, 0.1, 1, 10, 100]\n",
    "\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)\n",
    "\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "print ('The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))\n",
    "print ('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ca46e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 10.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[0.319449057556]</td>\n",
       "      <td>[0.322947055315]</td>\n",
       "      <td>RM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[0.229843609595]</td>\n",
       "      <td>[0.16743142495]</td>\n",
       "      <td>RAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.11164947562]</td>\n",
       "      <td>[0.0958485565994]</td>\n",
       "      <td>ZN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[0.0512456938248]</td>\n",
       "      <td>[0.0502616633623]</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.0265359794548]</td>\n",
       "      <td>[0.0288216344474]</td>\n",
       "      <td>CHAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-0.0379094943747]</td>\n",
       "      <td>[-0.0560070744824]</td>\n",
       "      <td>INDUS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[-0.0552504538354]</td>\n",
       "      <td>[-0.0558579461893]</td>\n",
       "      <td>AGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[-0.0829425704712]</td>\n",
       "      <td>[-0.0738229893482]</td>\n",
       "      <td>CRIM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-0.159177260109]</td>\n",
       "      <td>[-0.134971864727]</td>\n",
       "      <td>NOX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[-0.215616780333]</td>\n",
       "      <td>[-0.208999235448]</td>\n",
       "      <td>PTRATIO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-0.255123757229]</td>\n",
       "      <td>[-0.198331409258]</td>\n",
       "      <td>TAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[-0.313173772457]</td>\n",
       "      <td>[-0.279059228661]</td>\n",
       "      <td>DIS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[-0.372759366843]</td>\n",
       "      <td>[-0.360400624525]</td>\n",
       "      <td>LSTAT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               coef_lr          coef_ridge  columns\n",
       "5     [0.319449057556]    [0.322947055315]       RM\n",
       "8     [0.229843609595]     [0.16743142495]      RAD\n",
       "1      [0.11164947562]   [0.0958485565994]       ZN\n",
       "11   [0.0512456938248]   [0.0502616633623]        B\n",
       "3    [0.0265359794548]   [0.0288216344474]     CHAS\n",
       "2   [-0.0379094943747]  [-0.0560070744824]    INDUS\n",
       "6   [-0.0552504538354]  [-0.0558579461893]      AGE\n",
       "0   [-0.0829425704712]  [-0.0738229893482]     CRIM\n",
       "4    [-0.159177260109]   [-0.134971864727]      NOX\n",
       "10   [-0.215616780333]   [-0.208999235448]  PTRATIO\n",
       "9    [-0.255123757229]   [-0.198331409258]      TAX\n",
       "7    [-0.313173772457]   [-0.279059228661]      DIS\n",
       "12   [-0.372759366843]   [-0.360400624525]    LSTAT"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis=0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1))\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print('alpha is:', ridge.alpha_)\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list(lr.coef_.T), \"coef_ridge\":list(ridge.coef_.T)})\n",
    "fs.sort_values(by=['coef_lr'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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